Observing the Permanent-Magnet Temperature of Synchronous Motors Based on Electrical Fundamental Wave Model Quantities

Wallscheid, Oliver; Specht, Andreas; Böcker, Joachim

doi:10.1109/tie.2017.2652363

Cited by 73 publications

(24 citation statements)

References 33 publications

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“…The correction valueĝ t of gradient g t is introduced into the Nadam algorithm and compared with Adam at time t, and the gradientĝ t is defined by Formula (12). In addition, the updated gradient ∆θ t is calculated by Formula (13). Finally, the predicted output values can be obtained by the trained model.…”

Section: Model Construction and Predictionmentioning

confidence: 99%

“…Liu et al [12] analyzed the time dependence of ocean temperatures at multiple depths and proposed a time-dependent ocean temperature prediction method, and the test results showed a better predictive performance than both support vector regression (SVR) and a multilayer perceptron regressor (MLPR). Wallschied et al [13] verified the feasibility of LSTM on temperature prediction. However, the introduction of memory blocks in LSTM made the topological relationships complex, thus increasing the computing complexity.…”

Section: Introductionmentioning

confidence: 98%

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The Temperature Prediction of Permanent Magnet Synchronous Machines Based on Proximal Policy Optimization

et al. 2020

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Accurate temperature prediction plays an important role in the thermal protection of permanent magnet synchronous motors. A temperature prediction method of permanent magnet synchronous machines (PMSMs) based on proximal policy optimization is proposed. In the proposed method, the actor-critic framework of reinforcement learning is introduced to model the effective temperature prediction mechanism, and the correlations between the input features are then analyzed to select the appropriate input features. Finally, the simplified proximal policy optimization algorithm is introduced to optimize the value of the prediction temperature of PMSMs. Experimental results reveal the high accuracy and reliability of the proposed method compared with an exponential weighted moving average method (EWMA), a recurrent neural network (RNN), and long short-term memory (LSTM).

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Section: Model Construction and Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

The Temperature Prediction of Permanent Magnet Synchronous Machines Based on Proximal Policy Optimization

et al. 2020

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“…In order to ensure safe operation, many experts and scholars have put forward methods to measure the temperature of a PMSM. Wallscheid et al used an accurate flux observer in the fundamental wave domain that can indirectly obtain magnet temperature without any additional sensors or signal injections [4]. Mohammed et al proposed a new sensing method, namely, investigating the application of dedicated electrically non-conductive and electromagnetic interference immune fiber Bragg grating (FBG) temperature sensors embedded in PMSM windings to enable winding open-circuit fault diagnosis based on observing the fault thermal signature [5,6].…”

Section: Introductionmentioning

confidence: 99%

Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network

et al. 2020

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The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM) directly affect the its temperature rise. The accurate evaluation and prediction of stator winding temperature is of great significance to the safety and reliability of PMSMs. In order to study the influencing factors of stator winding temperature and prevent motor insulation ageing, insulation burning, permanent magnet demagnetization and other faults caused by high stator winding temperature, we propose a computer model for PMSM temperature prediction. Ambient temperature, coolant temperature, direct-axis voltage, quadrature-axis voltage, motor speed, torque, direct-axis current, quadrature-axis current, permanent magnet surface temperature, stator yoke temperature, and stator tooth temperature are taken as the input, while the stator winding temperature is taken as the output. A deep neural network (DNN) model for PMSM temperature prediction was constructed. The experimental results showed the prediction error of the model (MAE) was 0.1515, the RMSE was 0.2368, the goodness of fit (R2) was 0.9439 and the goodness of fit between the predicted data and the measured data was high. Through comparative experiments, the prediction accuracy of the DNN model proposed in this paper was determined to be better than other models. This model can effectively predict the temperature change of stator winding, provide technical support to temperature early warning systems and ensure safe operation of PMSMs.

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“…Furthermore, it is also possible to determine motor temperatures via temperature‐dependent machine parameters. The use of rotor flux linkage model‐based observer is proposed in [2–5] using the fact that (NeFeB) PM loses 0.11–0.12% remanence per one degree Celsius temperature rise. Nevertheless, this method is difficult to apply practically, because of the necessity of a precise modelling for motor and inverter — the model‐related errors otherwise will be misinterpreted as temperature changes.…”

Section: Introductionmentioning

confidence: 99%

PWM‐based Flux linkage estimation for permanent magnet synchronous machines

Xiao

Griffo

2019

J. eng.

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Observing the Permanent-Magnet Temperature of Synchronous Motors Based on Electrical Fundamental Wave Model Quantities

Cited by 73 publications

References 33 publications

The Temperature Prediction of Permanent Magnet Synchronous Machines Based on Proximal Policy Optimization

The Temperature Prediction of Permanent Magnet Synchronous Machines Based on Proximal Policy Optimization

Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network

PWM‐based Flux linkage estimation for permanent magnet synchronous machines

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